One promising way to address the mentioned issues is the use of population models within the higher-tier risk assessment (Forbes et al. 2008), which in my opinion, should be used in close combination with ecotoxicological experiments. The requirements for models in risk assessment are discussed by the scientific community (Preuss et al. 2009a). Population modelling was explicitly mentioned as a helpful and promising option in pesticide risk as- sessment by the ELINK and LEMTOX workshops (Brock et al. 2007; Forbes et al. 2009; Thorbek et al. 2009) and is now practically underlined by the CREAM project, a Marie Curie Training Network funded by the EU (Grimm et al. 2009).
Several questions can be formulated in order to form objectives and to derive a conceptual approach for this work:
• How to assess time-variable exposure patterns within the risk assessment of algae? • How to compare, for risk assessment purposes, simple exposure assumptions with
complex exposure profile in a time-variable scale?
• Is recovery of an algae population possible after pulsed exposure to pesticides? • Does time-variable exposure influence the sensitivity of the affected algae species? • Is it possible to predict effects of time-variable exposure on algae based on experi-
mental data, and what data is needed to fulfill any potential requirements?
• Can algae population- and effect modelling help to support a risk assessment for al- gae?
• What kind of experimental setup is required and what type of simulation model is ap- propriate to address these questions within the regulatory risk assessment?
The following objectives were declared to address the above questions:
• Definition of ecologically representative algae species and their most important physi- ological properties
• Selection of a herbicide that causes critical issues in the risk assessment
• Investigation and evaluation of available ecological, ecotoxicological and environmen- tal fate data for the selected algae species and the chosen herbicide
• Calculation of the substance-related FOCUS exposure patterns
• Performance of laboratory studies in order to close identified data gaps
Objectives
Objectives and Conceptual Approach 23
• Determination of the model parameter values by literature research, by evaluation of existing data sets or by performance of necessary experiments
• Sensitivity analysis of the model
• Definition, verification and validation of standardized parameter sets for the selected algae species
• Performance of laboratory experiments to generate independent data sets for model verification and validation
• Development of an appropriate experimental setup for the continuous cultivation of algae populations
• Test of the suitability of the experimental system to assess effects of pulsed exposure on algae
• Performance of experiments with algae populations exposed to the selected test sub- stance in a time-variable scale
• Simulation and prediction of the experimental data with the model
• Evaluation of the results of the combined experimental / modelling approach in a reg- ulatory context
The objectives of the work are illustrated by the following figure:
Figure 4-1: Conceptual approach Data evaluation Algae species Test substance Ecotoxicology Efate FOCUS exposure pattern Model
development experimentsLaboratory
Data
FOCUS exposure pattern Flow-through experiment
use data generate data
Algae ecology Algae toxicity Algae competition Time-Variable Exposure Model Implementation Parameter values Sensitivity analysis Verification & validation Standardized parameter sets
Combination of modelling & experiments
Evaluation of the results
4.2 Conceptual Approach
An important part of the present work aimed at the development, testing and application of such a model for algal populations. The model should be able to:
1) describe the influence of environmental conditions (light, temperature and nutrients) on algae population growth
2) reflect the effects of time-variable exposure of herbicides on different algae species. A model that is relatively simple and applicable for regulatory needs, as well as extendable towards more complex questions related to the biological reality, should own the following properties:
1) All implemented model parameters should rely on ecological processes and should not be just fitting parameters resulting from mathematical constructs.
2) All parameter values should be determinable by independent experiments.
3) Data generated within the framework of a risk assessment for pesticides should be usable for model input, with no need for further studies.
4) The user should be able to easily understand the simulated processes in the model and, by the simple composition of the model, transparency of the results should be given.
5) As good as the model results may seem to be, a model is only as good as the data on which it is based; i.e. validation of a model must be a necessary step in the model de- velopment and should be mandatory for an intended use under regulatory aspects. 6) The model to be developed during this work should not only be usable and valid for a
few isolated and specific cases. It should provide verification for its whole scope of application, based on a good foundation of independent data sets.
To parameterize, test, verify and validate the model in order to allow an application without the need of fitting or optimization, suitable experimental data had to be generated. A choice of appropriate algae species as test organisms for the experimental work, and to be de- scribed by the model, was one important topic. The algae species to be investigated during this work and subsequently being implemented in the model should cover the following spe- cific aspects:
• The species play a key role in aquatic ecosystems and are often abundant in meso- cosms.
• A full database of physiological properties and toxicity data is available for the select- ed species or can be generated within this work.
Conceptual Approach
Objectives and Conceptual Approach 25
• Each species is representative for a taxonomic group with structural and functional importance in an aquatic biocenosis
The attempt to address the aim of this work from the experimental side led to the develop- ment and application of a flow-through system based on the chemostat principle for the con- tinuous culture of algae. Therefore, the exposure of algae populations to a pesticide should be possible in a time-variable scale. It appeared advantageous that chemostats are self- regulating systems and no external interference is needed to operate the chemostats in a stable manner. In contrast to static experiments, it is possible to observe both the inhibition of algae growth directly as visible loss of biomass and the recovery of populations by re- establishing a steady-state condition in the system. The applicability of experimental flow- through systems for algae as higher-tier tools in pesticide risk assessment was investigated frequently (Aoyama and Okamura 1993; Wong et al. 1983; Dobbs et al. 1996). However, while pulsed exposure effects of herbicides on algae were evaluated under static conditions (Vallotton et al. 2008a-c, 2009) or flow-through systems were used to assess the effects of toxicants on algae (Halling-Sørensen et al. 1997; Grade et al. 2000; Hall et al. 1989), no combined use of such higher-tier systems together with model predictions, was reported in literature yet.
This work covers the development of a specific algae population model with all necessary experimental work as well as the construction, testing and application of a flow-through sys- tem, in close combination with the modelling approach. Questions of recovery-times after pulsed herbicide exposure, and of a potentially altered sensitivity of the algae to the pesti- cide, will be discussed and addressed in this context. Furthermore, the usability of the exper- imental / modelling approach in risk assessments for algae will be assessed based on the results.
I am sure that this work will increase the potential acceptability for ecological models in the pesticide risk assessment by presenting promising new approaches to assess effects of time-variable exposure on aquatic non-target organisms.